Artificial Intelligence
AI for Leaders
Overview
This course equips leaders with the knowledge and skills to navigate AI adoption within their organization to improve productivity and performance, establish proper AI governance and risk management processes, and lead AI strategy and change management. This course is provided as either a four-hour session as part of our AI Learning Subscription or a full day of training for student cohorts.
Upon completing this course, participants will be able to:
- Provide a clear understanding of AI fundamentals and current capabilities.
- Empower executives to create AI Vision & Strategy
- Facilitated discovery of organization-specific strategic AI applications
- Build awareness of the ethical, compliance, and safety considerations of AI implementation.
- Enhance productivity and efficiency through practical AI tools and techniques
Schedule
Register 21 days before class start date and save 10%! Enter discount code EARLY10 during registration.
Duration: 4 Hours
Dates
Times
Location
Price
Who Should Take This Course
Audience
Senior Executives, Senior Managers, People leaders, Innovators & Individual Contributors
Prerequisites
A passion for innovation, transformation and leadership. No AI background necessary.
Why You Should Take This Course
Upon completing this course, participants will be able to:
- Provide a clear understanding of AI fundamentals and current capabilities.
- Empower executives to create AI Vision & Strategy
- Facilitated discovery of organization-specific strategic AI applications
- Build awareness of the ethical, compliance, and safety considerations of AI implementation.
- Enhance productivity and efficiency through practical AI tools and techniques
Course Outline
AI for Leaders
Module 1: AI Foundations and Business Applications
- Understanding AI Fundamentals: Clarify key AI terms (machine learning, deep learning, natural language processing, etc.) in business-friendly terms, avoiding overly technical jargon.
- AI Use Cases Across Industries: Provide real-world examples of AI applications in different sectors (finance, healthcare, manufacturing, retail) to spark ideas.
- Exercise: Brainstorming AI Potential: Divide into small groups. Have each group pick an industry (aligned with corporate/agency undertaking) and generate 3-5 ways AI could transform it
Module 2: Building AI Vision & Strategy
- Strategic Alignment: Emphasize the importance of aligning AI initiatives with overall business goals and objectives.
- Evaluating AI Solutions: Provide a framework for comparing AI vendors, tools, and their fit within the organization.
- Data Readiness: Stress the need for quality data, data governance, and the role of data scientists.
- Change Management: Address preparing the workforce for AI, potential shifts in job roles, and continuous upskilling.
- Exercise: Drafting an AI Vision Statement: Have groups write a short vision statement outlining how they see AI transforming their organization within the next 3 years.
Module 3: AI-Driven Efficiency and Effectiveness
- Process Automation: Explore how AI can automate routine tasks, optimize workflows, and free up employees for higher-value work.
- Data-Driven Decision Making: Demonstrate the power of AI in analyzing large datasets to uncover patterns, trends, and make better-informed decisions.
- Predictive Modeling: Explain how AI can be used to forecast future scenarios, enabling proactive planning and strategy.
- Exercise: Process Mapping: Participants map out a key business process. The instructor then highlights areas ripe for AI-assisted streamlining.
Module 4: Ethics, Compliance, and Responsible AI
- Bias and Fairness: Discuss the dangers of algorithmic bias and how to proactively address it in AI models.
- Privacy and Security: Cover best practices for protecting data in the AI era, safeguarding personal information, and complying with regulations (GDPR, etc.)
- Transparency and Explainability: Highlight the importance of explainable AI models, especially in sensitive sectors.
- Responsible AI Frameworks: Introduce existing ethical AI guidelines and principles developed by various organizations and governments.
- AI Risk Management: Identify potential risks related to AI and set the stage for the development of an AI playbook based on NIST and other industry standard frameworks.
- Exercise: Ethical Case Study Analysis: Present a hypothetical scenario with an AI-related ethical dilemma. Groups discuss solutions, trade-offs, and risk mitigation.
Module 5: AI for Productivity and Innovation
- AI-Powered Collaboration Tools: Showcase examples of how AI can streamline communication, task management, and knowledge sharing.
- AI in Product/Service Development: Outline how AI can drive innovation and the development of new offerings.
- AI’s Future Impact: Briefly discuss transformative technologies like generative AI, and their potential to revolutionize industries.
- Exercise: Hands-on Use: Walk through some of the advanced features in everyday productivity tools.
Wrap-Up
- Q&A Session: Address any remaining questions and encourage participants to reflect on their learning.
- Reflections: How will the role of leadership evolve in the AI age?
- Action Planning: Provide a worksheet for executives to outline what resonated with them and document the next immediate steps they’ll take to explore AI implementation in their organizations.
- Resources: Share a list of valuable resources (industry research, white papers, ethical AI guidelines).
- Consultation: Discuss options for
- Immersive engagement at team-level to operationalize transformation
- Monthly mentoring session to operationalize AI innovation
- Quarterly lunch & learn with industry experts
- Join the AI Slack community
- Executive Networking Opportunities with UMBC c4a Board & Advisors (by invitation only)